This paper presents an integrated approach to segmenting moving foreground, where the moving foreground is of most interest to the viewer. Multiple cues are used-focus, intensity, and motion-in a two-layered neural network. Focus and motion measurements are taken from high frequency data, edges; whereas, intensity measurements are taken from low frequency date, object interiors. Combined, these measurements are used to segment a complete object. Results indicate that moving foreground can be segmented from stationary foreground and moving or stationary background. The neural network segments the entire object, both interior and exterior, in this integrated approach. Results also demonstrate that combining cues allows flexibility in both type and complexity of scenes. Integration of cues improves accuracy in segmenting complex scenes containing both moving foreground and background. Good segmentation yields bitrate savings when coding the object of interest, also called the video object in MPEG4. Our method combines simple measurements to increase segmentation robustness.